There are a number of approaches for estimating interaction effects
in SEM. In modsem()
, the method = "method"
argument allows you to choose which to use. Different approaches can be
categorized into two groups: Product Indicator (PI) and Distribution
Analytic (DA) approaches.
Product Indicator (PI) Approaches:

"ca"
= constrained approach (Algina & Moulder, 2001) Note that constraints can become quite complicated for complex models, particularly when there is an interaction including enodgenous variables. The method can therefore be quite slow.

"uca"
= unconstrained approach (Marsh, 2004) 
"rca"
= residual centering approach (Little et al., 2006) 
"dblcent"
= double centering approach (Marsh., 2013) default

"pind"
= basic product indicator approach (not recommended)
Distribution Analytic (DA) Approaches

"lms"
= The Latent Moderated Structural equations (LMS) approach, see the vignette 
"qml"
= The Quasi Maximum Likelihood (QML) approach, see the vignette 
"mplus"
 estimates model through Mplus, if it is installed
m1 < '
# Outer Model
X =~ x1 + x2 + x3
Y =~ y1 + y2 + y3
Z =~ z1 + z2 + z3
# Inner model
Y ~ X + Z + X:Z
'
# Product Indicator Approaches
modsem(m1, data = oneInt, method = "ca")
modsem(m1, data = oneInt, method = "uca")
modsem(m1, data = oneInt, method = "rca")
modsem(m1, data = oneInt, method = "dblcent")
# Distribution Analytic Approaches
modsem(m1, data = oneInt, method = "mplus")
modsem(m1, data = oneInt, method = "lms")
modsem(m1, data = oneInt, method = "qml")